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Developing a Sustainable Machine Learning Model to Predict Crop Yield in the Gulf Countries

Author

Listed:
  • Hamzeh F. Assous

    (Finance Department, School of Business, King Faisal University, Al Ahsa 31982, Saudi Arabia)

  • Hazem AL-Najjar

    (Department of Computer, Abdul Aziz Al Ghurair School of Advanced Computing (ASAC), Luminus Technical University College, Amman 11732, Jordan)

  • Nadia Al-Rousan

    (MIS Department, Faculty of Business, Sohar University, Sohar 311, Oman)

  • Dania AL-Najjar

    (Finance Department, School of Business, King Faisal University, Al Ahsa 31982, Saudi Arabia)

Abstract

Crop yield prediction is one of the most challenging tasks in agriculture. It is considered to play an important role and be an essential step in decision-making processes. The goal of crop prediction is to establish food availability for the coming years, using different input variables associated with the crop yield domain. This paper aims to predict the yield of five of the Gulf countries’ crops: wheat, dates, watermelon, potatoes, and maize (corn). Five independent variables were used to develop a prediction model, namely year, rainfall, pesticide, temperature changes, and nitrogen (N) fertilizer; all these variables are calculated by year. Moreover, this research relied on one of the most widely used machine learning models in the field of crop yield prediction, which is the neural network model. The neural network model is used because it can predict complex relationships between independent and dependent variables. To evaluate the performance of the prediction models, different statistical evaluation metrics are adopted, including mean square error (MSE), root-mean-square error (RMSE), mean bias error (MBE), Pearson’s correlation coefficient, and the determination coefficient. The results showed that all Gulf countries are affected mainly by four independent variables: year, temperature changes, pesticides, and nitrogen (N) per year. Moreover, the average of the best crop yield prediction results for the Gulf countries showed that the RMSE and R 2 are 0.114 and 0.93, respectively. This provides initial evidence regarding the capability of the neural network model in crop yield prediction.

Suggested Citation

  • Hamzeh F. Assous & Hazem AL-Najjar & Nadia Al-Rousan & Dania AL-Najjar, 2023. "Developing a Sustainable Machine Learning Model to Predict Crop Yield in the Gulf Countries," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9392-:d:1168600
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    References listed on IDEAS

    as
    1. Yamaç, Sevim Seda & Todorovic, Mladen, 2020. "Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data," Agricultural Water Management, Elsevier, vol. 228(C).
    2. Hasan Arda Burhan, 2022. "Crop Yield Prediction by Integrating Meteorological and Pesticides Use Data with Machine Learning Methods: An Application for Major Crops in Turkey," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 7(SI), pages 1-18.
    3. J. Jed Brown & Probir Das & Mohammad Al-Saidi, 2018. "Sustainable Agriculture in the Arabian/Persian Gulf Region Utilizing Marginal Water Resources: Making the Best of a Bad Situation," Sustainability, MDPI, vol. 10(5), pages 1-16, April.
    Full references (including those not matched with items on IDEAS)

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